Stochastic Risk-Constrained Scheduling of Renewable-Powered Autonomous Microgrids With Demand Response Actions: Reliability and Economic Implications

Vahedipour‐Dahraie, Mostafa; Rashidizadeh‐Kermani, Homa; Anvari‐Moghaddam, Amjad

doi:10.1109/tia.2019.2959549

Cited by 72 publications

(34 citation statements)

References 36 publications

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“…In this regard, a microgrid (MG) is projected as a localized consumer-interactive distribution network construction within the smart grid community, to achieve a low-carbon society with reduced greenhouse gas emissions, while taking into account the local-generation properties, variability in the generation inputs and economic aspects [5]- [7]. MGs comprise different types of distributed generation (DG) units, energy storage systems, and electrical loads, and can operate either with the grid (grid-connected mode) or without the grid (islanded mode) [8], [9] to create an efficient and more economical system with enhanced power quality and reliability performance levels, increased energy efficiency, and reduced environmental pollution [10], [11]. From the perspective of economic aspects, MG operators have to determine the optimal energy management (EM) that can accomplish the lowest operating, maintenance and capital costs over the lifetime of a project [12], while maintaining reliability, efficiency, and power quality considerations of production and consumption of electricity.…”

Section: Grid−tmentioning

confidence: 99%

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Robust Energy Management and Economic Analysis of Microgrids Considering Different Battery Characteristics

et al. 2020

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This paper presents the economic analysis and optimal energy management of a grid-connected MG that comprises renewable energy resources and different battery storage technologies with different characteristics such as initial charge, depth of discharge, and the number of charging/discharging cycles to minimize the total operating cost of the system by maximizing the benefits of BSS, minimizing the investment and replacement cost of BSS, and minimizing the operation and maintenance cost of DGs. Several constraints are considered, such as the output power limits of the distributed generators, the limits of power imported from or exported to the grid, load balance, and other sets of battery storage constraints. The general algebraic modeling system (GAMS) is used to solve the deterministic optimization problem. Second, stochastic optimization is used to solve the deterministic problem with market price uncertainty. Third, robust optimization using the information gap decision theory is presented to model the electric load uncertainty. The validity and effectiveness of the proposed solution are explained by comparing the results obtained by GAMS to the results obtained by other optimization techniques presented in the literature.

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Section: Grid−tmentioning

confidence: 99%

“…FCM is the objective function value at iteration m. The stochastic operation cost ($/kWh) of the grid at time t considers 5 market price scenarios (S) and their probabilities (Ä s ), which are obtained by FCM, expressed in (37) to solve the objective function given in (8).…”

Section: Stochastic Optimization To Take Account Of Market Price Umentioning

confidence: 99%

Robust Energy Management and Economic Analysis of Microgrids Considering Different Battery Characteristics

et al. 2020

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“…While renewable sources are used for the generation, this makes them a problem to be addressed by the control system due to their time-varying nature, difficulty in predicting, and lack of manipulative capability. Although the controller cannot adjust these variables (except for in the case of Demand Response) [10], MPC may use the current information (current measurement and future prediction) to forecast the system output along the horizon.…”

Section: A Background and Motivationmentioning

confidence: 99%

Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

Gbadega

Saha

2020

IEEE Access

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The design and implementation of appropriate advanced control strategies is a key factor for the effective integration of micro-grids into the electrical network. In view of this, the study proposes an Adaptive Model-Based Horizon Control technique in the bid to addressing issues related to the Energy Management System in micro-grid operations. The main objective of the energy management system is to balance energy generation and demand through energy storage, so as to optimize the operation of the microgrid with high penetrations of renewable energy sources. This paper further investigates the impacts of considering the prediction of disturbances on the performance of the Energy Management System based on the adaptive model predictive control algorithm in order to improve the operating costs of the micro-grid with hybrid-energy storage systems. The adaptive model predictive control algorithm solves the energy optimization problem in a renewable energy-based micro-grid with various types of energy storage systems that exchange energy with the host grid. More so, this optimization problem is resolved at each sampling period in order to determine the minimum running costs while satisfying demand and taking into account technical and physical constraints. The simulation results under different conditions have demonstrated how the use of an adaptive model predictive control based energy management system can enhance micro-grid operation, provided there is effective forecasting, and consequently minimized the running operating costs of micro-grid. More so, it is evident in the cost function, , obtained from the three scenarios conducted, that the perfect knowledge of the disturbance prediction is essential for effective micro-grid operations.

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“…Similarly, rooftop renewable energy generation also helps in the fulfillment of high rise energy demand [7], [8]. The uncertainty during the fulfillment of the high rise energy demand through renewable energy generation is considered by Vahedipour in [9]. In this model, the authors used a stochastic risk-constrained framework to maximize the expected profit of the microgrid operator through the optimal scheduling of renewable resources.…”

Section: Introductionmentioning

confidence: 99%

Joint Energy Management and Energy Trading in Residential Microgrid System

et al. 2020

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The sustainability of the power systems assures consumers to have efficient and cost-effective energy consumption. Consumers' energy management is one of the solutions that in fact boosts the power system stability via efficiently scheduling the appliances. In addition to energy management, consumers fulfill their low-cost energy consumption using decentralized energy generation (such as solar, wind, plugin hybrid electric vehicles, and small diesel generator). This decentralized energy generation and its trading among the prosumers and consumers help in the distribution grid stability and continuous supply. In this paper, the joint energy management and energy trading model is presented, which provides low-cost electricity consumption to the distribution system. The proposed framework is a twofold system. In the first fold, the distribution system is divided into a number of microgrids, where each microgrid electricity demand is managed using a unified energy management approach. While the local energy produced is traded among the microgrids in the second fold, through energy trading concepts that fulfill the consumers' demand without stressing the utility company. The results indicate that the proposed model reduced the electricity cost of the microgrids with maximum share of self-generation. Moreover, the results also indicate that each microgrid either fulfills its electricity demand from self-generation or purchases it from the nearby microgrid. INDEX TERMS Smartgrid, Unified demand side management, Peak to average power ratio, Consumer comfort level.. Nomenclature β 1 , β 2 Set of appliances having various priorities, e.g., β 1 ∈ {Washing machine, dish washer} and β 2 ∈{Dryer, sterilizer}, etc. γ t Peak clipping maximum limit. λ t,n v,a Consumer preference factor A n Set of appliances of consumer's n.

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Stochastic Risk-Constrained Scheduling of Renewable-Powered Autonomous Microgrids With Demand Response Actions: Reliability and Economic Implications

Cited by 72 publications

References 36 publications

Robust Energy Management and Economic Analysis of Microgrids Considering Different Battery Characteristics

Robust Energy Management and Economic Analysis of Microgrids Considering Different Battery Characteristics

Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

Joint Energy Management and Energy Trading in Residential Microgrid System

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